Multimodal artificial intelligence for prostate cancer imaging: workflow-relevant fusion of mpMRI, PSMA PET, ultrasound, and clinical data for diagnosis, local staging, and treatment personalization.
Aug 2026· Abdominal Radiology· 0 citations· 31 references
Medicine
TL;DR
The most adoption-ready directions for Abdominal Radiology readers are modular multimodal systems that improve triage, guide biopsy targeting, and quantify local extension risk with transparent validation pathways and human-centered deployment design.
This review summarizes the deep-learning architectures, fusion strategies, representative applications, and implementation challenges of mpMRI-centered multimodal AI.
M. Fujiwara, Soichiro Yoshida, Imon Banerjee et al.· Abdominal Radiology· 0 citations
PURPOSE
Multiparametric MRI (mpMRI)-ultrasound (US) fusion biopsy misses 8%-20% of clinically significant prostate cancer (csPCa), leaving a detection gap that prostate-specific membrane antigen (PSMA) PET/CT may help close. Whether incorporating PSMA into an MRI-US fusion platform improves csPCa detection has not been...
Chen Huang, Li-Chen Jin, Yi-Zhang Sun et al.· European Journal of Nuclear...· 0 citations
Prostate-specific membrane antigen (PSMA)-targeted PET/CT (PSMA PET/CT) has become the first-line imaging modality used for staging and restaging prostate cancer. Its utility after minimally invasive focal therapy (high-intensity focused ultrasound, laser ablation, cryoablation) remains unclear. This study evaluated PS...
Mahbod Jafarvand, A. Farolfi, Riccardo Mei et al.· Journal of Nuclear Medicine· 0 citations
The evolving role of nuclear medicine physicians and radiologists as integrators of AI-derived biomarkers are highlighted, who validate AI outputs for high-stakes decisions, retain interpretive authority for complex cases, and oversee quality assurance, ensuring that AI augments rather than replaces specialist expertis...
Yiting Wang, Chao Cheng, Bing-Sheng Huang et al.· Japanese Journal of Radiolog...· 0 citations
The aim of this review was to summarize current evidence on AI-based imaging analysis, radiomics, and pathomics for PCa detection, characterization, risk stratification, and pathological assessment, while highlighting the methodological challenges that currently limit clinical implementation.
R. Rahota, Andrei-Vlad Bădulescu, B. Buhas et al.· Journal of Clinical Medicine· 0 citations
RATIONALE AND OBJECTIVES
To develop and validate a brain imaging-based multimodal radiomics model for noninvasive prediction of pathological subtypes in lung cancer patients with brain metastases, and to evaluate the effects of imaging modalities, computed tomography, contrast-enhanced T1-weighted imaging, T2-weighted ...
Jia-Shi Geng, Jian-Yu Xu, Hong-Kai Liu et al.· Academic Radiology· 0 citations
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